Machine learning applications typically involve use of purpose-built hardware that analyzes data collected from one or more separate hardware devices. This is typically accomplished in a manner where the data collection process is disjoint from the analysis and learning processes. Moreover, the analysis and learning processes are typically accomplished in systems that are purpose-built complex arrangements of high-cost computing hardware and software infrastructure. While such solutions might be effective at applying learning algorithms to data, important analysis, assessment and control capabilities are sometimes missing. These missing capabilities might prevent current solutions from being well suited for applying learning and analysis techniques to data as it is collected at the edge where this information is created. Due to the disjoint nature of data collection, analysis and learning in conventional systems, the data to which algorithms are applied is “stale” at the time of consumption. As a result conventional systems might have limited applicability to real-time data.